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Optimizing Engagement in Digital Mental Health Services among Sexual and Gender Minority Consumers

优化性少数群体消费者对数字心理健康服务的参与

基本信息

批准号：
10738476
负责人：
Meghan Romanelli
金额：
$ 13.84万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-06-21 至 2027-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10738476
关键词：
Address Adult Advocacy Affective Anxiety Behavioral Caring Characteristics Cognitive Communities Data Analyses Data Analytics Development Discrimination Distal Ethnic Origin Evaluation Expectancy Experimental Designs Feedback Focus Groups Funding Future Gender Identity Goals Health Health Resources Health Services Health Services Accessibility Health Services Research Health behavior change Healthcare Heterogeneity Heterosexuals Improve Access Informatics Infrastructure Internet Intervention K-Series Research Career Programs Mental Depression Mental Health Mental Health Services Mental health promotion Metadata Methodology Methods Modeling Names National Institute of Mental Health Outcome Pattern Personal Satisfaction Population Positioning Attribute Principal Investigator Process Public Health Race Reporting Research Research Support Route Safety Sampling Self Direction Services Sex Orientation Sexual and Gender Minorities Sexual and Gender Minority Youth Shapes Social Work Source Strategic Planning Subgroup Support Groups Symptoms Technology Testing Therapeutic Training Universities Washington analytical method barrier to care big data management care preference career cisgender data integration design digital intervention digital mental health evidence base experience feasibility testing gender minority group gender minority health health care availability health disparity health equity health equity promotion health goals help-seeking behavior human centered design improved innovation insight marginalized population member peer preference professor prototype psychoeducation psychoeducational randomized trial screening self reliance service delivery sexual identity supervised learning theories tool trial design

项目摘要

PROJECT SUMMARY Sexual and gender minority (SGM) populations are disproportionately impacted by mental health concerns relative to their heterosexual and cisgender peers. SGM populations, however, continue to report unmet mental health needs because they cannot or do not access mental health services. Digital Mental Health (DMH) has been recognized as a feasible, economical, and effective approach to broaden the availability of mental health care to consumers who face barriers to mental health help-seeking. SGM consumers cite a preference for DMH resources, and this delivery format holds promise to attend to major access barriers experienced by this consumer group. Yet, the availability of DMH content tailored to the needs of SGM consumers is limited, and a dearth of research examines SGM populations' actual engagement with DMH services. A potential solution to fully understand how SGM populations utilize DMH services would be to characterize their engagement within a natural setting. Leveraging an established partnership with Mental Health America (MHA), a non-profit mental health advocacy group offering free, evidence-based screenings and self-guided DMH resources, this study will follow a large, naturalistic sample of SGM DMH consumers with the aims to: 1) characterize consumers' current and predictive patterns of engagement with MHA's DMH resources and examine consumer characteristics associated with these different engagement patterns (Aim 1); 2) employ human-centered design approaches to gain insights related to group-specific processes not captured by MHA's meta-data that impact DMH engagement among SGM consumers and integrate data to design and build prototype engagement strategies for consumer evaluation (Aim 2), and; 3) test the tailored engagement strategies with MHA's SGM consumers using a micro-randomized trial (MRT) design (Aim 3). To accomplish these aims and prepare for a larger R01 trial, the Principal Investigator will receive training in: 1) the theoretical, substantive, and methodological underpinnings of DMH; 2) big data management and analytic methods, specifically supervised machine learning; 3) human-centered design approaches for designing tailored DMH interventions, and; 4) experimental design and data analysis for evaluating adaptive DMH interventions. The candidate is an Assistant Professor of Social Work at the University of Washington whose long-term career goal is to become an expert in the field of mental health services research, specializing in digital interventions to improve service access for SGM consumers through increased availability and enhanced acceptability. The proposed study advances this objective by aligning with the care preferences of SGM consumers, leveraging the infrastructure of an existing DMH platform at minimal burden to consumers, and contributing evidence towards optimized engagement outcomes for SGM consumers through the use of tailored strategies.

项目总结